Prediction of mine water flow based on singular spectrum analysis and multiple time-series coupled model

2021 ◽  
Vol 14 (24) ◽  
Author(s):  
Xiao Yang ◽  
Peihe Zhai ◽  
Longqing Shi ◽  
Xiaoge Yu ◽  
Xingyue Qu ◽  
...  
Keyword(s):  
Time Series ◽  
Water Flow ◽  
Spectrum Analysis ◽  
Mine Water ◽  
Singular Spectrum Analysis ◽  
Coupled Model ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Singular Spectrum

scholarly journals Using Multichannel Singular Spectrum Analysis to Study Galaxy Dynamics

2020 ◽  
Author(s):  
Martin D Weinberg ◽  
Michael S Petersen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
Multiple Time ◽  
New Approach ◽  
Galaxy Dynamics ◽  
Singular Spectrum ◽  
Pattern Speed ◽  
Formation And Evolution

Abstract N-body simulations provide most of our insight into the structure and evolution of galaxies, but our analyses of these are often heuristic and from simple statistics. We propose a method that discovers the dynamics in space and time together by finding the most correlated temporal signals in multiple time series of basis function expansion coefficients and any other data fields of interest. The method extracts the dominant trends in the spatial variation of the gravitational field along with any additional data fields through time. The mathematics of this method is known as multichannel singular spectrum analysis (M-SSA). In essence, M-SSA is a principal component analysis of the covariance of time series replicates, each lagged successively by some interval. The dominant principal component represents the trend that contains the largest fraction of the correlated signal. The next principal component is orthogonal to the first and contains the next largest fraction, and so on. Using a suite of previously analysed simulations, we find that M-SSA describes bar formation and evolution, including mode coupling and pattern-speed decay. We also analyse a new simulation tailored to study vertical oscillations of the bar using kinematic data. Additionally, and to our surprise, M-SSA uncovered some new dynamics in previously analysed simulations, underscoring the power of this new approach.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

scholarly journals Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

2018 ◽  
Vol 17 (02) ◽  
pp. 1850017 ◽  
Author(s):  
Mahdi Kalantari ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani ◽  
Emmanuel Sirimal Silva
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Missing Values ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
L1 Norm ◽  
Nonparametric Approach ◽  
Singular Spectrum ◽  
Simulated Time

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the [Formula: see text] norm-based version of Singular Spectrum Analysis (SSA), namely [Formula: see text]-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially [Formula: see text]-SSA can provide better imputation in comparison to other methods.

scholarly journals Evidence of structured Brownian dynamics from temperature time series analysis

1997 ◽  
Vol 4 (4) ◽  
pp. 251-254
Author(s):  
A. Pasini ◽  
V. Pelino ◽  
S. Potestà
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spectrum Analysis ◽  
Brownian Dynamics ◽  
Singular Spectrum Analysis ◽  
Temperature Time Series ◽  
Singular Spectrum ◽  
The Usa ◽  
Temperature Time ◽  
Analysis Of Time Series

Abstract. An analysis of time series of monthly mean temperatures ranging from 1895 to 1989 is performed through application of Singular Spectrum Analysis (SSA) to data of several places in the USA. A common dynamics in the reconstructed spaces is obtained, with the evidence of a non-trivial and structured coupling of two Brownian motions, resembling the so-called Lévy flights. The idea that these two correlated functions are related to the zonal and eddy components of the atmospheric motions is suggested.

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